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When it comes to finding actionable insights out of data to drive decision-making capabilities and accelerate understanding of the business better and hence serve customers in the best feasible way, data analytics is the domain that comes into the picture.
Data Analytics is a collective, integrated, and collaborative approach that enables organizations to leverage various tools and technologies to become data-driven.
Big Data Analytics
With digital transformation, customers interacting with no digital products are growing exponentially; hence, the amount of data organizations must deal with is also growing.
Performing analytics on such a huge amount of data while taking care of variety, velocity, veracity, and value refers to big data analytics.
While dealing with big data analytics, challenges can come along the way in terms of collection, storage, cataloging, governance, analytics, and visualization of such a huge amount of data.
Hence, a general flow or set of steps used to be employed that are arranged properly to enable big data analytics, which is called a big data pipeline. The set of steps, phases, or components of the pipeline are as follows:
- Collection and Ingestion: Data sources must be identified, and appropriate ingestion techniques can be used to collect data.
- Storage: Based on the nature of the data, data can be ingested and stored in appropriate repositories such as data lakes, databases, or data warehouses.
- Cataloging and Governance: Data about data,e., metadata, is important, and its existence opens the door for ad-hoc exploration.
- Processing: Raw data is not a good option to perform analytics on. It must be transformed, if needed, to be in a decent shape that drives better analytics.
- Analytics and Visualization: Finally, the data is ready for analysis and creating visualization.
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Data Analytics on AWS:
Considering the 5Vs and the need to satisfy the requirements of the components or phases of the big data pipeline, traditional or open-source tools, and technologies mostly used on-premises have limitations and challenges in terms of deployment, use, scalability, reliability, security, cost, management overhead, etc.
AWS has a big portfolio of data analytics services that organizations can use to build highly secure, highly performant, cost-effective, and scalable data analytics solutions on AWS Cloud.
- Collection and Ingestion: AWS DMS (Database Migration Service), AWS SCT, Amazon Kinesis, Amazon MSK (Managed Streaming for Apache Kafka) (Managed Streaming for Apache Kafka), AWS DataSync, AWS Transfer Family
- Storage: Amazon S3, AWS Purpose-Built Databases, Amazon EMR HDFS (Hadoop Distributed File System)
- Cataloging, Processing, and Governance: AWS Glue, AWS Lake Formation, Amazon EMR, AWS Lambda
- Analytics and Visualization: Amazon Athena, Amazon QuickSight, Amazon Redshift
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Some of the prominent services in action, including Amazon S3, Amazon EC2, Amazon Redshift, Amazon RDS (Relational Database Service)
- Salesforce: https://www.youtube.com/watch?v=pjAGZQbhm-A
Some of the prominent services in action include Amazon EMR and AWS DataSync.
Some prominent services include Amazon S3, Amazon RDS, AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift.
Leveraged Amazon OpenSearch
A wide portfolio of data analytics services offered by AWS is helping organizations build modern data platforms. Building Data Lakes, Cloud-based Data Warehouse Solutions, and Real Time Streaming solutions is no longer challenging and can be easily built on AWS.
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WRITTEN BY Muhammad Imran